The Curious Case of NIRB's Acquisition of Jurisdiction Over Scientific Research in Nunavut
Bibliographic record
Abstract
By virtue of Article 12.2.2 of the Nunavut Land Claims Agreement, the Nunavut Impact Review Board (NIRB) is empowered to “screen project proposals in order to determine whether or not a review is required.” That the NIRB has the jurisdiction to screen such proposals in respect of Major Development Projects is generally not contested or in dispute. The case is not so clear with respect to scientific research project proposals and, more specifically, whether the NIRB properly has the jurisdiction to screen scientific research proposals in the same manner as it does Major Development Projects. While recognizing that the problem this question of jurisdiction poses is more of a legal one than a practical one, this article concludes that NIRB’s jurisdiction to screen (and, if necessary, review) scientific research proposals is not entirely clear, incompletely regulated, and likely lacking. In the absence of such clarity respecting jurisdiction, the article contemplates that decisions of the NIRB respecting scientific research proposals may be called into question in the form of judicial review and present future problems for the territory and its inhabitants if left unaddressed. Also proposed are simple solutions that could alleviate some of the problems identified in the article.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.015 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".